第5期 孙正兴，等：基于局部SM分类器的表情识别方法 ·463- kemel and k in LSVM were detem ined by 10-fold First,we tested the facial expresson recognition coss validation on the training set To mplement the accuracy based on our proposed classifier LSVM.Con- proposed LSVM algorithm,we modified the C++code fusion matrices were used to evaluate accuracy The of the L BSVM (http://www.csie ntu edu w/cjlin/ confusion matrix is a matrix containing infomation a- libsvm)tool developed by Chang and L in to use Co as bout the actual class label in its columns)and the la- its upper bound constraint for a instead of c bel obtained through classification in its rows).The In order to make maxmal use of the available data diagonal entries of the confusion matrix are the rates of and produce averaged classification accuracy results, correctly classified facial expressions,while the off-di- the expermental results reported in this study were ob- agonal entries correspond to m isclassification rates tained based on app lying a 5-fold cross validation to the The confusion matrix shown in Table 2 presents the re- data sets More specifically,all mage sequences con- sults obtained while using the LSVM classifier From tained in the database were divided into six classes,this table,it can be seen that our method achieves each one corresponding to one of the six basic facial 89 11%overall recognition of facial expressions The expressions b be recognized Five sets containing 20% confusion matrix confims that some expressions are of the data for each class,chosen random ly,were cre- harder to differentiate than others Expressions identi- ated One set containing 20%of the samples for each fied as surprise or happiness are recognized with the class was used for the test set,while the remaining sets highest accuracy (91.32%and 92 48%).For dis- fomed the training set After the classification proce- gust,the recognition rate was 88 32%,for fear it was dure was perfomed,the samples fom ing the testing 89.64%,for sadness is 86.38%,and for anger is set were incorporated into the current training set,and 86 54%.As can be seen,the most ambiguous facial a new set of samples (20%of the samples for each expression was sadness The main reason is that both class)was extracted b fom the new test set The re- surprise and happ iness cause obvious geometric shape maining samples fomed a new training set This proce- changes when the facial expression moves from neutral dure was repeated five tmes The average classification to peak,while othersmay not produce enough geomet- accuracy is the mean value of the percentages of the ric infomation to be as clearly discrm inated correctly classified fac ial expressions Table 2 Confusion matrix based on LSVM Inputs Results (% Happy Surprise Disgust Fear Sad Anger Happy 91.32 179 211 032 258 188 Surprise 268 9248 183 132 0 1.69 Disgust 209 217 8832 308 1.08 3.26 Fear 0 0 357 8964 436 243 Sad 1.62 356 246 178 8638 420 Anger 229 0 171 386 5.60 8654 In addition,we conducted experments to evaluate rithm.The accuracy of SVM is 85.06%while the ac- the perfomance of our proposed algorithms in compari- curacy of KNN is only 78.96%.Secondly,observe son KNN,nonlinear SVM and SVMNN algorithms that SVMNN fails to mp rove the accuracy over nonlin- A 5-fold cross validation was also emp byed in these ex-ear SVM.In fact,the SVMNN perfomance degrades, perments Table 3 summarizes the results of our exper as classification accuracy drops from 85.06%to ments Firstly,observe that,for the six basic facial 85.03%when using SVMNN instead of nonlinear exp ressions,nonlinear SVM outperfoms the KNN algo- SVM.One possible explanation for the poor perfom- 1994-2009 China Academic Journal Electronic Publishing House.All rights reserved.http://www.cnki.netkernel and k in LSVM were determ ined by 102fold cross validation on the training set. To imp lement the p roposed LSVM algorithm, we modified the C ++ code of the L IBSVM ( http: / /www. csie. ntu. edu. tw / cjlin / libsvm) tool developed by Chang and Lin to use Cσ as its upper bound constraint forα instead of c. In order to make maximal use of the available data and p roduce averaged classification accuracy results, the experimental results reported in this study were ob2 tained based on app lying a 52fold cross validation to the data sets. More specifically, all image sequences con2 tained in the database were divided into six classes, each one corresponding to one of the six basic facial exp ressions to be recognized. Five sets containing 20% of the data for each class, chosen random ly, were cre2 ated. One set containing 20% of the samp les for each class was used for the test set, while the remaining sets formed the training set. After the classification p roce2 dure was performed, the samp les form ing the testing set were incorporated into the current training set, and a new set of samp les ( 20% of the samp les for each class) was extracted to form the new test set. The re2 maining samp les formed a new training set. This p roce2 dure was repeated five times. The average classification accuracy is the mean value of the percentages of the correctly classified facial exp ressions. First, we tested the facial exp ression recognition accuracy based on our p roposed classifier LSVM. Con2 fusion matrices were used to evaluate accuracy. The confusion matrix is a matrix containing information a2 bout the actual class label ( in its columns) and the la2 bel obtained through classification ( in its rows). The diagonal entries of the confusion matrix are the rates of correctly classified facial exp ressions, while the off2di2 agonal entries correspond to m isclassification rates. The confusion matrix shown in Table 2 p resents the re2 sults obtained while using the LSVM classifier. From this table, it can be seen that our method achieves 89. 11% overall recognition of facial exp ressions. The confusion matrix confirm s that some exp ressions are harder to differentiate than others. Exp ressions identi2 fied as surp rise or happ iness are recognized with the highest accuracy ( 91. 32% and 92. 48% ). For dis2 gust, the recognition rate was 88. 32% , for fear it was 89. 64% , for sadness is 86. 38% , and for anger is 86. 54%. A s can be seen, the most ambiguous facial exp ression was sadness. The main reason is that both surp rise and happ iness cause obvious geometric shape changes when the facial exp ression moves from neutral to peak, while othersmay not p roduce enough geomet2 ric information to be as clearly discrim inated. Table 2 Confusion ma tr ix ba sed on LSVM Inputs Results ( % ) Happy Surp rise D isgust Fear Sad Anger Happy 91. 32 1. 79 2. 11 0. 32 2. 58 1. 88 Surp rise 2. 68 92. 48 1. 83 1. 32 0 1. 69 D isgust 2. 09 2. 17 88. 32 3. 08 1. 08 3. 26 Fear 0 0 3. 57 89. 64 4. 36 2. 43 Sad 1. 62 3. 56 2. 46 1. 78 86. 38 4. 20 Anger 2. 29 0 1. 71 3. 86 5. 60 86. 54 In addition, we conducted experiments to evaluate the performance of our p roposed algorithm s in compari2 son to KNN, nonlinear SVM and SVM2NN algorithm s. A 52fold cross validation was also emp loyed in these ex2 periments. Table 3 summarizes the results of our exper2 iments. Firstly, observe that, for the six basic facial exp ressions, nonlinear SVM outperform s the KNN algo2 rithm. The accuracy of SVM is 85. 06% while the ac2 curacy of KNN is only 78. 96%. Secondly, observe that SVM2NN fails to imp rove the accuracy over nonlin2 ear SVM. In fact, the SVM2NN performance degrades, as classification accuracy drop s from 85. 06% to 85. 03% when using SVM2NN instead of nonlinear SVM. One possible exp lanation for the poor perform2 第 5期 孙正兴 ,等 :基于局部 SVM分类器的表情识别方法 ·463·